An asymmetric encoder–decoder model for Zn-ion battery lifetime prediction
As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the negative reaction inside the battery, generate irreversible chemical substances, and reduce the number of active substances involved in the e...
Saved in:
| Published in: | Energy reports Vol. 8; pp. 33 - 50 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.12.2022
Elsevier |
| Subjects: | |
| ISSN: | 2352-4847, 2352-4847 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the negative reaction inside the battery, generate irreversible chemical substances, and reduce the number of active substances involved in the electrochemical reaction, resulting in a decrease in battery capacity. Batteries that lose 20% of their capacity can be considered to have failed. A failed battery shows that the battery capacity and power decay faster, and the electrical characteristics, stability, and safety of the battery will drop significantly. As a means of improving the machine learning model’s accuracy and generalization for RUL prediction of zinc-ion batteries, this paper mainly discusses about the design of the encoder–decoder model structure and the application of optimization methods. Then, the method of neural network hyperparameter optimization is studied. Finally, the validity of the research work done in this paper is verified by a series of comparative experiments. |
|---|---|
| AbstractList | As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the negative reaction inside the battery, generate irreversible chemical substances, and reduce the number of active substances involved in the electrochemical reaction, resulting in a decrease in battery capacity. Batteries that lose 20% of their capacity can be considered to have failed. A failed battery shows that the battery capacity and power decay faster, and the electrical characteristics, stability, and safety of the battery will drop significantly. As a means of improving the machine learning model’s accuracy and generalization for RUL prediction of zinc-ion batteries, this paper mainly discusses about the design of the encoder–decoder model structure and the application of optimization methods. Then, the method of neural network hyperparameter optimization is studied. Finally, the validity of the research work done in this paper is verified by a series of comparative experiments. |
| Author | Yin, Zhengtong Liao, Shengjun Yin, Lirong Yang, Bo Liu, Mingzhe Liu, Shan Zheng, Wenfeng Lu, Siyu |
| Author_xml | – sequence: 1 givenname: Siyu surname: Lu fullname: Lu, Siyu organization: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China – sequence: 2 givenname: Zhengtong surname: Yin fullname: Yin, Zhengtong organization: College of Resource and Environment Engineering, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 3 givenname: Shengjun surname: Liao fullname: Liao, Shengjun organization: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China – sequence: 4 givenname: Bo surname: Yang fullname: Yang, Bo organization: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China – sequence: 5 givenname: Shan surname: Liu fullname: Liu, Shan organization: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China – sequence: 6 givenname: Mingzhe surname: Liu fullname: Liu, Mingzhe email: liumz@cdut.edu.cn organization: School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China – sequence: 7 givenname: Lirong surname: Yin fullname: Yin, Lirong organization: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA – sequence: 8 givenname: Wenfeng orcidid: 0000-0002-8486-1654 surname: Zheng fullname: Zheng, Wenfeng email: winfirms@uestc.edu.cn organization: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China |
| BookMark | eNp9kMtKAzEUhoMoWGtfwFVeYMYkk7kE3JTipVBwoxs3IZczJcNcSiYI3fkOvqFPYqZVEBfdnHPI4fvJ-a7QeT_0gNANJSkltLhtUtjufcoIYykRKaP0DM1YlrOEV7w8_zNfosU4NoQQKhjhRTZDm2WP1bjvOgjeGQy9GSz4r49PC4cJd7G2uB48fusTN_RYqxDA73HragiuA7zzYJ0JcXeNLmrVjrD46XP0-nD_snpKNs-P69VykxhOSUh0UZYMbJEJq4uccsgh40apmtjcgCiBgaKCK22sybICmGHaaC1yS4ppn83R-phrB9XInXed8ns5KCcPD4PfSuWDMy1Iy0l0xKoaNHAjREVzAFWxmF9TW01Z1THL-GEcPdTSuKCma4JXrpWUyEmybOQkWU6SJREySo4o-4f-fuUkdHeEIAp6d-DlaFzUHh16MCFe4E7h3xwrmmc |
| CitedBy_id | crossref_primary_10_1016_j_synthmet_2022_117263 crossref_primary_10_1109_ACCESS_2025_3535520 crossref_primary_10_1016_j_jtice_2023_104685 crossref_primary_10_1016_j_mssp_2024_109089 crossref_primary_10_1016_j_est_2023_107760 crossref_primary_10_1016_j_est_2023_107244 crossref_primary_10_1016_j_est_2023_107489 crossref_primary_10_1016_j_est_2023_107402 crossref_primary_10_1016_j_mtchem_2023_101660 crossref_primary_10_3390_sym15010210 crossref_primary_10_1016_j_egyr_2023_03_095 crossref_primary_10_1016_j_est_2024_110827 crossref_primary_10_1016_j_ijhydene_2023_03_274 crossref_primary_10_1016_j_ijhydene_2022_11_279 crossref_primary_10_1016_j_matchemphys_2023_127447 crossref_primary_10_1016_j_scp_2023_101115 crossref_primary_10_1016_j_inoche_2023_110785 crossref_primary_10_1016_j_apenergy_2023_121224 crossref_primary_10_1016_j_ijepes_2023_109478 crossref_primary_10_1016_j_matchemphys_2025_130611 crossref_primary_10_1016_j_enganabound_2022_11_004 crossref_primary_10_1016_j_heliyon_2023_e19387 crossref_primary_10_1016_j_est_2022_106530 crossref_primary_10_1016_j_ijepes_2023_109228 crossref_primary_10_1007_s10854_023_09952_0 crossref_primary_10_3390_en16186505 crossref_primary_10_1016_j_inoche_2022_110371 crossref_primary_10_1016_j_fuel_2023_128457 crossref_primary_10_3390_su152416722 crossref_primary_10_1016_j_enganabound_2023_01_038 crossref_primary_10_1016_j_susmat_2024_e01045 crossref_primary_10_1080_15376494_2023_2286499 crossref_primary_10_3390_math11071751 crossref_primary_10_1016_j_est_2023_107789 crossref_primary_10_1016_j_seta_2023_103287 crossref_primary_10_3390_en18030746 crossref_primary_10_3390_coatings13010147 crossref_primary_10_1016_j_psep_2023_03_056 crossref_primary_10_3390_nano14030271 crossref_primary_10_1016_j_ijhydene_2023_02_016 crossref_primary_10_1002_ese3_1441 crossref_primary_10_1007_s12034_024_03247_8 crossref_primary_10_1080_15376494_2023_2270525 crossref_primary_10_1016_j_est_2023_107490 crossref_primary_10_1016_j_physb_2023_415027 crossref_primary_10_1016_j_suscom_2023_100899 crossref_primary_10_1080_08927022_2023_2165125 crossref_primary_10_1016_j_inoche_2022_110385 crossref_primary_10_1016_j_physb_2023_414977 crossref_primary_10_1080_08927022_2022_2161586 crossref_primary_10_1016_j_est_2023_107257 crossref_primary_10_1016_j_jpcs_2024_112301 crossref_primary_10_1016_j_est_2022_106548 crossref_primary_10_1016_j_est_2023_106844 crossref_primary_10_3390_en16227637 crossref_primary_10_1016_j_mssp_2023_107383 crossref_primary_10_1016_j_jmgm_2023_108423 crossref_primary_10_1016_j_seta_2022_102913 crossref_primary_10_1016_j_jpcs_2023_111548 crossref_primary_10_1016_j_cis_2023_102865 crossref_primary_10_1016_j_ijhydene_2023_01_340 crossref_primary_10_1016_j_enganabound_2022_12_029 crossref_primary_10_1007_s12010_022_04305_9 crossref_primary_10_1016_j_epsr_2023_109389 crossref_primary_10_1016_j_ijhydene_2023_01_106 crossref_primary_10_1016_j_seta_2023_103461 crossref_primary_10_1016_j_rser_2023_113440 crossref_primary_10_1007_s12633_023_02452_0 crossref_primary_10_1016_j_ijhydene_2023_03_356 crossref_primary_10_1016_j_applthermaleng_2022_119878 crossref_primary_10_1016_j_mssp_2024_109059 crossref_primary_10_1016_j_enganabound_2023_04_017 crossref_primary_10_1016_j_ijhydene_2022_12_264 crossref_primary_10_3390_e25010134 crossref_primary_10_1016_j_jcou_2023_102395 crossref_primary_10_1016_j_est_2023_108485 crossref_primary_10_1016_j_psep_2023_06_052 crossref_primary_10_1109_ACCESS_2023_3263264 crossref_primary_10_1016_j_heliyon_2023_e17634 crossref_primary_10_1016_j_apsusc_2023_156757 crossref_primary_10_1049_gtd2_12875 crossref_primary_10_1016_j_applthermaleng_2023_120853 crossref_primary_10_1016_j_est_2022_106450 crossref_primary_10_1016_j_electacta_2023_142654 crossref_primary_10_1016_j_est_2022_106570 crossref_primary_10_1049_rpg2_12902 crossref_primary_10_3390_en16020642 crossref_primary_10_1016_j_inoche_2022_110296 crossref_primary_10_1016_j_est_2023_108276 crossref_primary_10_1016_j_est_2023_108039 crossref_primary_10_1016_j_ijhydene_2022_12_018 crossref_primary_10_1016_j_est_2023_109888 crossref_primary_10_1016_j_arabjc_2023_104709 crossref_primary_10_3390_en16031494 crossref_primary_10_1016_j_synthmet_2022_117234 crossref_primary_10_1038_s41598_023_35659_7 crossref_primary_10_1016_j_inoche_2023_110480 crossref_primary_10_3390_electronics12214521 crossref_primary_10_3390_sym15040844 crossref_primary_10_1016_j_mtcomm_2023_106887 crossref_primary_10_1016_j_ijhydene_2023_01_254 crossref_primary_10_1016_j_ijhydene_2023_01_132 crossref_primary_10_1016_j_enganabound_2023_01_006 crossref_primary_10_1016_j_comptc_2022_113982 crossref_primary_10_1007_s00894_023_05555_y crossref_primary_10_1016_j_inoche_2023_110767 |
| Cites_doi | 10.1007/s00521-020-05150-9 10.1109/TIE.2017.2782224 10.1109/JLT.2014.2299070 10.1016/j.physd.2019.132306 10.1109/ICASSP.2017.7952603 10.1134/S1064226916040112 10.1177/016344378300500205 10.21437/Interspeech.2012-65 10.1109/TCYB.2018.2863108 10.1016/S0893-6080(05)80030-9 10.1007/978-3-030-02922-7_31 10.1109/TIM.2008.2005965 10.1109/YAC.2016.7804912 10.1109/JAS.2017.7510727 10.1109/ICCV.2019.00658 10.1109/MWSCAS.2017.8053243 10.4319/lo.2010.55.3.1047 10.1145/1755952.1755987 10.1016/j.ress.2014.04.023 10.1016/j.microrel.2017.06.045 10.1093/comjnl/3.3.175 10.1162/neco_a_01199 10.1109/TIP.2018.2886767 10.1109/TEC.2006.874229 10.1016/S0006-3495(79)85319-9 |
| ContentType | Journal Article |
| Copyright | 2022 The Author(s) |
| Copyright_xml | – notice: 2022 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.egyr.2022.09.211 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Open Access: DOAJ - Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2352-4847 |
| EndPage | 50 |
| ExternalDocumentID | oai_doaj_org_article_d4010128febe4c99815eea82194f1d87 10_1016_j_egyr_2022_09_211 S2352484722019357 |
| GroupedDBID | 0R~ 0SF 4.4 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB GROUPED_DOAJ KQ8 M41 M~E NCXOZ O9- OK1 ROL SSZ AAYWO AAYXX ACVFH ADCNI ADVLN AEUPX AFJKZ AFPUW AIGII AKBMS AKRWK AKYEP APXCP CITATION |
| ID | FETCH-LOGICAL-c410t-b6772ed639db6514e5e34caaf0d5ce97e2ea194abcdc336e2c2bcbb95d06ce973 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 119 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000887145000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2352-4847 |
| IngestDate | Fri Oct 03 12:34:49 EDT 2025 Thu Nov 13 04:36:07 EST 2025 Tue Nov 18 21:00:27 EST 2025 Tue May 16 21:55:24 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Asymmetric encoder–decoder model Zinc-ion battery Battery life prediction |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c410t-b6772ed639db6514e5e34caaf0d5ce97e2ea194abcdc336e2c2bcbb95d06ce973 |
| ORCID | 0000-0002-8486-1654 |
| OpenAccessLink | https://doaj.org/article/d4010128febe4c99815eea82194f1d87 |
| PageCount | 18 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d4010128febe4c99815eea82194f1d87 crossref_citationtrail_10_1016_j_egyr_2022_09_211 crossref_primary_10_1016_j_egyr_2022_09_211 elsevier_sciencedirect_doi_10_1016_j_egyr_2022_09_211 |
| PublicationCentury | 2000 |
| PublicationDate | December 2022 2022-12-00 2022-12-01 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: December 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Energy reports |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Wang, Wang, Liu, Wei (b10) 2017; 5 R. Dey, F.M. Salem, Gate-variants of gated recurrent unit (GRU) neural networks, in: Proceedings of the 2017 IEEE 60th international midwest symposium on circuits and systems, MWSCAS, 2017, pp. 1597–1600, 2017. Bornholdt, Graudenz (b7) 1992; 5 Holtgrieve, Schindler, Branch, A’mar (b20) 2010; 55 Liu, Li, Fang, Qi, Shen, Zhou, Zhang (b14) 2021; 125 Chorowski, Bahdanau, Serdyuk, Cho, Bengio (b13) 2015 J. Huang, Z. Li, N. Li, S. Liu, G. Li, Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism, in: Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6480–6489, 2019. Yu, Si, Hu, Zhang (b24) 2019; 31 Rosenbrock (b30) 1960; 3 Beck, Katafygiotis (b22) 1998; 124 Wren-Lewis (b9) 1983; 5 Luong, Pham (b23) 2015 Alex Townsend, Constrained optimization in Chebfun. Hu, Li, Peng (b3) 2017; 198 Stroud, Agard (b8) 1979; 25 Savchenko, Savchenko (b11) 2016; 61 X. Wu, Y. Cai, Q. Li, J. Xu, H.-f. Leung, Combining contextual information by self-attention mechanism in convolutional neural networks for text classification, in: Proceedings of the international conference on web information systems engineering, 2018, pp. 453–467, 2018. Han, Li, Zhang, Ahmad (b12) 2018; 50 Li, Zeng, Shan, Chen (b15) 2018; 28 Sherstinsky (b26) 2020; 404 P. Zuliani, A. Platzer, E.M. Clarke, Bayesian statistical model checking with application to simulink/stateflow verification, in: Proceedings of the 13th ACM international conference on hybrid systems: computation and control, 2010, pp. 243–252, 2010. Wei, Dong, Chen (b5) 2018; 65 Matsumoto, Kodama, Shimizu, Nomura, Omichi, Wada, Kitayama (b6) 2014; 32 Song, Liu, Yang (b4) 2017; 75 M. Sundermeyer, R. Schlüter, H. Ney, LSTM neural networks for language modeling, in: Proceedings of the thirteenth annual conference of the international speech communication association, 2012, 2012. R. Fu, Z. Zhang, L. Li, Using LSTM and GRU neural network methods for traffic flow prediction, in: Proceedings of the 2016 31st youth academic annual conference of Chinese Association of Automation, YAC, 2016, pp. 324–328, 2016. B. Athiwaratkun, J.W. Stokes, Malware classification with LSTM and GRU language models and a character-level CNN, in: Proceedings of the 2017 IEEE international conference on acoustics, speech and signal processing, ICASSP, 2017, pp. 2482–2486, 2017. Min, Rincon-Mora Gabriel (b1) 2006; 21 Saha, Goebel, Poll (b2) 2009; 58 Park, Grandhi (b21) 2014; 129 2014. Ju, Luo, Wang, Luo (b16) 2021; 33 Han (10.1016/j.egyr.2022.09.211_b12) 2018; 50 Wei (10.1016/j.egyr.2022.09.211_b5) 2018; 65 Wren-Lewis (10.1016/j.egyr.2022.09.211_b9) 1983; 5 Wang (10.1016/j.egyr.2022.09.211_b10) 2017; 5 Chorowski (10.1016/j.egyr.2022.09.211_b13) 2015 Stroud (10.1016/j.egyr.2022.09.211_b8) 1979; 25 10.1016/j.egyr.2022.09.211_b17 10.1016/j.egyr.2022.09.211_b18 Li (10.1016/j.egyr.2022.09.211_b15) 2018; 28 10.1016/j.egyr.2022.09.211_b19 Yu (10.1016/j.egyr.2022.09.211_b24) 2019; 31 10.1016/j.egyr.2022.09.211_b31 Luong (10.1016/j.egyr.2022.09.211_b23) 2015 Ju (10.1016/j.egyr.2022.09.211_b16) 2021; 33 Sherstinsky (10.1016/j.egyr.2022.09.211_b26) 2020; 404 Matsumoto (10.1016/j.egyr.2022.09.211_b6) 2014; 32 Min (10.1016/j.egyr.2022.09.211_b1) 2006; 21 10.1016/j.egyr.2022.09.211_b25 10.1016/j.egyr.2022.09.211_b27 Hu (10.1016/j.egyr.2022.09.211_b3) 2017; 198 10.1016/j.egyr.2022.09.211_b28 10.1016/j.egyr.2022.09.211_b29 Song (10.1016/j.egyr.2022.09.211_b4) 2017; 75 Bornholdt (10.1016/j.egyr.2022.09.211_b7) 1992; 5 Liu (10.1016/j.egyr.2022.09.211_b14) 2021; 125 Park (10.1016/j.egyr.2022.09.211_b21) 2014; 129 Holtgrieve (10.1016/j.egyr.2022.09.211_b20) 2010; 55 Rosenbrock (10.1016/j.egyr.2022.09.211_b30) 1960; 3 Beck (10.1016/j.egyr.2022.09.211_b22) 1998; 124 Savchenko (10.1016/j.egyr.2022.09.211_b11) 2016; 61 Saha (10.1016/j.egyr.2022.09.211_b2) 2009; 58 |
| References_xml | – volume: 21 start-page: 504 year: 2006 end-page: 511 ident: b1 article-title: Accurate electrical battery model capable of predicting runtime and I–V performance publication-title: IEEE Trans Energy Convers – reference: Alex Townsend, Constrained optimization in Chebfun. – reference: J. Huang, Z. Li, N. Li, S. Liu, G. Li, Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism, in: Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6480–6489, 2019. – volume: 61 start-page: 430 year: 2016 end-page: 435 ident: b11 article-title: Information-theoretic analysis of efficiency of the phonetic encoding–decoding method in automatic speech recognition publication-title: J. Commun. Technol. Electron. – volume: 50 start-page: 36 year: 2018 end-page: 47 ident: b12 article-title: Impulsive consensus of multiagent systems with limited bandwidth based on encoding–decoding publication-title: IEEE Trans Cybern – reference: B. Athiwaratkun, J.W. Stokes, Malware classification with LSTM and GRU language models and a character-level CNN, in: Proceedings of the 2017 IEEE international conference on acoustics, speech and signal processing, ICASSP, 2017, pp. 2482–2486, 2017. – reference: . 2014. – volume: 129 start-page: 46 year: 2014 end-page: 56 ident: b21 article-title: A Bayesian statistical method for quantifying model form uncertainty and two model combination methods publication-title: Reliab Eng Syst Saf – volume: 404 year: 2020 ident: b26 article-title: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network publication-title: Physica D – year: 2015 ident: b23 article-title: Effective approaches to attention-based neural machine translation publication-title: Comput. Ence – volume: 25 start-page: 495 year: 1979 end-page: 512 ident: b8 article-title: Structure determination of asymmetric membrane profiles using an iterative Fourier method publication-title: Biophys J – reference: M. Sundermeyer, R. Schlüter, H. Ney, LSTM neural networks for language modeling, in: Proceedings of the thirteenth annual conference of the international speech communication association, 2012, 2012. – volume: 31 start-page: 1235 year: 2019 end-page: 1270 ident: b24 article-title: A review of recurrent neural networks: LSTM cells and network architectures publication-title: Neural Comput – reference: X. Wu, Y. Cai, Q. Li, J. Xu, H.-f. Leung, Combining contextual information by self-attention mechanism in convolutional neural networks for text classification, in: Proceedings of the international conference on web information systems engineering, 2018, pp. 453–467, 2018. – volume: 58 start-page: 291 year: 2009 end-page: 296 ident: b2 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: IEEE Trans Instrum Meas – volume: 75 start-page: 142 year: 2017 end-page: 153 ident: b4 article-title: Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery publication-title: Microelectron. Reliab. – year: 2015 ident: b13 article-title: Attention-based models for speech recognition publication-title: Adv Neural Inf Process Syst – volume: 65 start-page: 5634 year: 2018 end-page: 5643 ident: b5 article-title: Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression publication-title: IEEE Trans Ind Electron – reference: R. Fu, Z. Zhang, L. Li, Using LSTM and GRU neural network methods for traffic flow prediction, in: Proceedings of the 2016 31st youth academic annual conference of Chinese Association of Automation, YAC, 2016, pp. 324–328, 2016. – volume: 28 start-page: 2439 year: 2018 end-page: 2450 ident: b15 article-title: Occlusion aware facial expression recognition using CNN with attention mechanism publication-title: IEEE Trans Image Process – volume: 32 start-page: 1132 year: 2014 end-page: 1143 ident: b6 article-title: 40G-OCDMA-PON system with an asymmetric structure using a single multi-port and sampled SSFBG encoder/decoders publication-title: J Lightwave Technol – volume: 5 start-page: 179 year: 1983 end-page: 197 ident: b9 article-title: The encoding/decoding model: criticisms and redevelopments for research on decoding publication-title: Media Culture Soc. – volume: 3 start-page: 175 year: 1960 end-page: 184 ident: b30 article-title: An automatic method for finding the greatest or least value of a function publication-title: Comput J – volume: 5 start-page: 327 year: 1992 end-page: 334 ident: b7 article-title: General asymmetric neural networks and structure design by genetic algorithms publication-title: Neural Netw – volume: 5 start-page: 3 year: 2017 end-page: 18 ident: b10 article-title: Encoder–decoder-based control and filtering of networked systems: insights, developments and opportunities publication-title: IEEE/CAA J Autom Sin – volume: 125 year: 2021 ident: b14 article-title: Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network publication-title: Autom Constr – volume: 124 start-page: 455 year: 1998 end-page: 461 ident: b22 article-title: Updating models and their uncertainties. I: Bayesian statistical framework publication-title: J Eng Mech – reference: R. Dey, F.M. Salem, Gate-variants of gated recurrent unit (GRU) neural networks, in: Proceedings of the 2017 IEEE 60th international midwest symposium on circuits and systems, MWSCAS, 2017, pp. 1597–1600, 2017. – volume: 55 start-page: 1047 year: 2010 end-page: 1063 ident: b20 article-title: Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics publication-title: Limnol Oceanogr – volume: 198 start-page: 359 year: 2017 end-page: 367 ident: b3 article-title: A comparative study of equivalent circuit models for Li-ion batteries publication-title: J Power Sources – volume: 33 start-page: 2769 year: 2021 end-page: 2781 ident: b16 article-title: Adaptive feature fusion with attention mechanism for multi-scale target detection publication-title: Neural Comput Appl – reference: P. Zuliani, A. Platzer, E.M. Clarke, Bayesian statistical model checking with application to simulink/stateflow verification, in: Proceedings of the 13th ACM international conference on hybrid systems: computation and control, 2010, pp. 243–252, 2010. – volume: 124 start-page: 455 issue: 4 year: 1998 ident: 10.1016/j.egyr.2022.09.211_b22 article-title: Updating models and their uncertainties. I: Bayesian statistical framework publication-title: J Eng Mech – volume: 33 start-page: 2769 issue: 7 year: 2021 ident: 10.1016/j.egyr.2022.09.211_b16 article-title: Adaptive feature fusion with attention mechanism for multi-scale target detection publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05150-9 – volume: 65 start-page: 5634 issue: 7 year: 2018 ident: 10.1016/j.egyr.2022.09.211_b5 article-title: Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2017.2782224 – volume: 32 start-page: 1132 year: 2014 ident: 10.1016/j.egyr.2022.09.211_b6 article-title: 40G-OCDMA-PON system with an asymmetric structure using a single multi-port and sampled SSFBG encoder/decoders publication-title: J Lightwave Technol doi: 10.1109/JLT.2014.2299070 – volume: 404 year: 2020 ident: 10.1016/j.egyr.2022.09.211_b26 article-title: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network publication-title: Physica D doi: 10.1016/j.physd.2019.132306 – ident: 10.1016/j.egyr.2022.09.211_b29 doi: 10.1109/ICASSP.2017.7952603 – volume: 61 start-page: 430 year: 2016 ident: 10.1016/j.egyr.2022.09.211_b11 article-title: Information-theoretic analysis of efficiency of the phonetic encoding–decoding method in automatic speech recognition publication-title: J. Commun. Technol. Electron. doi: 10.1134/S1064226916040112 – volume: 5 start-page: 179 issue: 2 year: 1983 ident: 10.1016/j.egyr.2022.09.211_b9 article-title: The encoding/decoding model: criticisms and redevelopments for research on decoding publication-title: Media Culture Soc. doi: 10.1177/016344378300500205 – year: 2015 ident: 10.1016/j.egyr.2022.09.211_b13 article-title: Attention-based models for speech recognition publication-title: Adv Neural Inf Process Syst – ident: 10.1016/j.egyr.2022.09.211_b25 doi: 10.21437/Interspeech.2012-65 – volume: 50 start-page: 36 issue: 1 year: 2018 ident: 10.1016/j.egyr.2022.09.211_b12 article-title: Impulsive consensus of multiagent systems with limited bandwidth based on encoding–decoding publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2018.2863108 – volume: 5 start-page: 327 year: 1992 ident: 10.1016/j.egyr.2022.09.211_b7 article-title: General asymmetric neural networks and structure design by genetic algorithms publication-title: Neural Netw doi: 10.1016/S0893-6080(05)80030-9 – ident: 10.1016/j.egyr.2022.09.211_b18 doi: 10.1007/978-3-030-02922-7_31 – volume: 58 start-page: 291 issue: 2 year: 2009 ident: 10.1016/j.egyr.2022.09.211_b2 article-title: Prognostics methods for battery health monitoring using a Bayesian framework publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2008.2005965 – ident: 10.1016/j.egyr.2022.09.211_b27 doi: 10.1109/YAC.2016.7804912 – volume: 198 start-page: 359 issue: Jan.15 year: 2017 ident: 10.1016/j.egyr.2022.09.211_b3 article-title: A comparative study of equivalent circuit models for Li-ion batteries publication-title: J Power Sources – volume: 5 start-page: 3 issue: 1 year: 2017 ident: 10.1016/j.egyr.2022.09.211_b10 article-title: Encoder–decoder-based control and filtering of networked systems: insights, developments and opportunities publication-title: IEEE/CAA J Autom Sin doi: 10.1109/JAS.2017.7510727 – ident: 10.1016/j.egyr.2022.09.211_b17 doi: 10.1109/ICCV.2019.00658 – ident: 10.1016/j.egyr.2022.09.211_b28 doi: 10.1109/MWSCAS.2017.8053243 – volume: 55 start-page: 1047 issue: 3 year: 2010 ident: 10.1016/j.egyr.2022.09.211_b20 article-title: Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics publication-title: Limnol Oceanogr doi: 10.4319/lo.2010.55.3.1047 – ident: 10.1016/j.egyr.2022.09.211_b19 doi: 10.1145/1755952.1755987 – ident: 10.1016/j.egyr.2022.09.211_b31 – volume: 129 start-page: 46 year: 2014 ident: 10.1016/j.egyr.2022.09.211_b21 article-title: A Bayesian statistical method for quantifying model form uncertainty and two model combination methods publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2014.04.023 – volume: 75 start-page: 142 issue: aug year: 2017 ident: 10.1016/j.egyr.2022.09.211_b4 article-title: Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2017.06.045 – volume: 3 start-page: 175 issue: 3 year: 1960 ident: 10.1016/j.egyr.2022.09.211_b30 article-title: An automatic method for finding the greatest or least value of a function publication-title: Comput J doi: 10.1093/comjnl/3.3.175 – volume: 31 start-page: 1235 issue: 7 year: 2019 ident: 10.1016/j.egyr.2022.09.211_b24 article-title: A review of recurrent neural networks: LSTM cells and network architectures publication-title: Neural Comput doi: 10.1162/neco_a_01199 – volume: 28 start-page: 2439 year: 2018 ident: 10.1016/j.egyr.2022.09.211_b15 article-title: Occlusion aware facial expression recognition using CNN with attention mechanism publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2018.2886767 – volume: 125 issue: 3 year: 2021 ident: 10.1016/j.egyr.2022.09.211_b14 article-title: Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network publication-title: Autom Constr – volume: 21 start-page: 504 issue: 2 year: 2006 ident: 10.1016/j.egyr.2022.09.211_b1 article-title: Accurate electrical battery model capable of predicting runtime and I–V performance publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2006.874229 – volume: 25 start-page: 495 year: 1979 ident: 10.1016/j.egyr.2022.09.211_b8 article-title: Structure determination of asymmetric membrane profiles using an iterative Fourier method publication-title: Biophys J doi: 10.1016/S0006-3495(79)85319-9 – year: 2015 ident: 10.1016/j.egyr.2022.09.211_b23 article-title: Effective approaches to attention-based neural machine translation publication-title: Comput. Ence |
| SSID | ssj0001920463 |
| Score | 2.5575323 |
| Snippet | As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 33 |
| SubjectTerms | Asymmetric encoder–decoder model Battery life prediction Zinc-ion battery |
| Title | An asymmetric encoder–decoder model for Zn-ion battery lifetime prediction |
| URI | https://dx.doi.org/10.1016/j.egyr.2022.09.211 https://doaj.org/article/d4010128febe4c99815eea82194f1d87 |
| Volume | 8 |
| WOSCitedRecordID | wos000887145000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 2352-4847 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920463 issn: 2352-4847 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2352-4847 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920463 issn: 2352-4847 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV27TsMwFLVQxcCCQIAoL3lgQxGN47zGgloxQMUAUsVi-XGDWrUBtQWpC-If-EO-hHudtCpLWViiKHFs69wkPpavz2HsnFZmkkjKIExxiiIhloGWSORMCjqCREBopDebSHu9rN_P71esvignrJIHroC7dJJU0ERWYGvS4uQgjAF0hh-aLEKX-X3kyHpWJlPDireQFJZ3lotFIPEfXO-YqZK74HlOYqBCkMipCMNfo5IX718ZnFYGnO4O266ZIm9XPdxlG1Dusdt2yfV0Ph6TEZblpELpYPL9-eXAn3HvbMORifKnMkDMufECmnM-GhRARvL8dUJrMxSPffbY7Txc3wS1IUJgZdiaBSZBLgwOSYUzCTIdiCGSVuui5WILeQoCNOKijXU2ihIQVhhrTB67VkL3owPWKF9KOGRcapEaC1GhMy1Tk5jcRtqYIsk0ZMi6mixcAKJsrRZOphUjtUgLGyoCURGIqpUrBLHJLpbPvFZaGWtLXxHOy5Kkc-0vYPRVHX31V_SbLF5ESdWUoaICWNVgTeNH_9H4MduiKqvklhPWmE3e4JRt2vfZYDo58y8kHu8-Oj_Nc-Zx |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+asymmetric+encoder%E2%80%93decoder+model+for+Zn-ion+battery+lifetime+prediction&rft.jtitle=Energy+reports&rft.au=Siyu+Lu&rft.au=Zhengtong+Yin&rft.au=Shengjun+Liao&rft.au=Bo+Yang&rft.date=2022-12-01&rft.pub=Elsevier&rft.issn=2352-4847&rft.eissn=2352-4847&rft.volume=8&rft.spage=33&rft.epage=50&rft_id=info:doi/10.1016%2Fj.egyr.2022.09.211&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_d4010128febe4c99815eea82194f1d87 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2352-4847&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2352-4847&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2352-4847&client=summon |